Martin-Molinero
3eec4a0090
- Add example of scheduled universe selection with custom data. See https://github.com/QuantConnect/Lean/pull/7895
87 lines
4.1 KiB
Python
87 lines
4.1 KiB
Python
# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
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# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from AlgorithmImports import *
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### <summary>
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### Custom data universe selection regression algorithm asserting it's behavior. Similar to CustomDataUniverseRegressionAlgorithm but with a custom schedule
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### </summary>
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class CustomDataUniverseScheduledRegressionAlgorithm(QCAlgorithm):
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def Initialize(self):
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'''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''
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self.SetStartDate(2014, 3, 24)
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self.SetEndDate(2014, 3, 31)
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self._selectionTime = [datetime(2014, 3, 25), datetime(2014, 3, 27), datetime(2014, 3, 29)]
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self.UniverseSettings.Resolution = Resolution.Daily;
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self.UniverseSettings.Schedule.On(self.DateRules.On(self._selectionTime))
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self.AddUniverse(CoarseFundamental, "custom-data-universe", self.UniverseSettings, self.Selection)
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# This use case is also valid/same because it will use the algorithm settings by default
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# self.AddUniverse(CoarseFundamental, "custom-data-universe", self.Selection)
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def Selection(self, coarse):
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self.Debug(f"Universe selection called: {self.Time} Count: {len(coarse)}")
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expectedTime = self._selectionTime.pop(0)
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if expectedTime != self.Time:
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raise ValueError(f"Unexpected selection time {self.Time} expected {expectedTime}")
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# sort descending by daily dollar volume
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sortedByDollarVolume = sorted(coarse, key=lambda x: x.DollarVolume, reverse=True)
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# return the symbol objects of the top entries from our sorted collection
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underlyingSymbols = [ x.Symbol for x in sortedByDollarVolume[:10] ]
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customSymbols = []
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for symbol in underlyingSymbols:
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customSymbols.append(Symbol.CreateBase(MyPyCustomData, symbol))
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return underlyingSymbols + customSymbols
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def OnData(self, data):
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'''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
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Arguments:
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data: Slice object keyed by symbol containing the stock data
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'''
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if not self.Portfolio.Invested:
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customData = data.Get(MyPyCustomData)
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symbols = [symbol for symbol in data.Keys if symbol.SecurityType is SecurityType.Equity]
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for symbol in symbols:
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self.SetHoldings(symbol, 1 / len(symbols))
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if len([x for x in customData.Keys if x.Underlying == symbol]) == 0:
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raise ValueError(f"Custom data was not found for symbol {symbol}")
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class MyPyCustomData(PythonData):
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def GetSource(self, config, date, isLiveMode):
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source = f"{Globals.DataFolder}/equity/usa/daily/{LeanData.GenerateZipFileName(config.Symbol, date, config.Resolution, config.TickType)}"
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return SubscriptionDataSource(source, SubscriptionTransportMedium.LocalFile, FileFormat.Csv)
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def Reader(self, config, line, date, isLiveMode):
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csv = line.split(',')
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_scaleFactor = 1 / 10000
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custom = MyPyCustomData()
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custom.Symbol = config.Symbol
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custom.Time = datetime.strptime(csv[0], '%Y%m%d %H:%M')
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custom.Open = float(csv[1]) * _scaleFactor
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custom.High = float(csv[2]) * _scaleFactor
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custom.Low = float(csv[3]) * _scaleFactor
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custom.Close = float(csv[4]) * _scaleFactor
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custom.Value = float(csv[4]) * _scaleFactor
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custom.Period = Time.OneDay
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custom.EndTime = custom.Time + custom.Period
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return custom
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